In 2014, the American Academy of Dermatology highlighted my skin self-examination (SSE) research as a promising direction for further study. There is a clear need for innovative SSE research as skin cancer rates have increased steadily over the past 30 years. SSE is the advocated approach for detecting skin cancer - for example, most melanomas are initially detected by patients - but two problems undermine its efficacy: (1) validation studies have found low agreement between patient SSE classifications and dermatological examinations and (2) attempts to improve lay ability to perform SSE have produced marginal gains. Given the drawbacks of traditional SSE, I propose to study an innovative alternative: communal feedback. Communal feedback is the use of collective effort to perform a task typically carried out by a single agent. It is especially effective when a single agent has low reliability at a task as the pattern of the group is more predictive than a single-user. Communal feedback occurs when the results of collective effort are conveyed back to the individual. For example, contestants on game shows use communal feedback when they ask the audience for help. When participants ask the audience for help, they receive feedback in the form of poll data showing what percent of the group supports a given answer. The participant then has to decide if the pattern of the group reflects reality. As an initial test of his strategy in skin cancer control, my lab trained 500 adults to identify suspicious moles using the ABCDEs. Following training, the participants were shown high resolution images of 40 moles (9 of which were clinically diagnosed melanomas) and asked to circle those they found suspicious. Consistent with the collective effort approach, the pattern of the group was more predictive of melanoma (sensitivity = .90, specificity = .72) than the average individual user (sensitivity = .58 specificity = .81). Specifically, if 19% of participants (or more) identified a mole as suspicious, then collective effort correctly identified 90% of melanomas, and correctly classified 72% of non-melanomas. Through the New Innovator grant, I propose to carry out four studies validating and translating communal feedback as an approach to skin cancer control. Communication researchers have argued that individuals can benefit from communal feedback. Communal feedback is an innovative strategy that attempts to build on the potential of new communication technology. Accordingly, this project will also test a model for translating communal feedback to other health arenas. For example, communal feedback could be utilized by radiologists deciphering patient scan data (e.g., mammograms), patients seeking feedback about the normality of medication side-effects, and healthcare providers diagnosing patient symptoms.